RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
Fan Lu, Guang Chen*, Yinlong Liu, Zhongnan Qu, Alois Knoll
Advances in Neural Information Processing Systems (NeurIPS), 2020
Keypoint detector and descriptor are two main components of point cloud registration.
Previous learning-based keypoint detectors rely on saliency estimation for each point or farthest point sample (FPS) for candidate points selection,
which are inefficient and not applicable in large scale scenes. This paper proposes Random Sample-based Keypoint Detector and Descriptor Network (RSKDD-Net) for large
scale point cloud registration. The key idea is using random sampling to efficiently select candidate points and using a learning-based method to jointly generate keypoints and descriptors.
To tackle the information loss of random sampling, we exploit a novel random dilation cluster strategy to enlarge the receptive field of each
sampled point and an attention mechanism to aggregate the positions and features of neighbor points.
Furthermore, we propose a matching loss to train the descriptor in a weakly supervised manner. Extensive experiments on two large scale outdoor
LiDAR datasets show that the proposed RSKDD-Net achieves state-of-the-art performance with more than 15 times faster than existing methods.
The network architecture is shown below.
Several qualitative results can be seen below.